With billions of dollars and euros invested annually in utility infrastructure and the associated asset base, the need to make the right asset management and maintenance decisions is critical.
It is also becoming increasingly challenging. Asset managers are tasked with effectively selecting which maintenance, replacement or growth projects will bring the most value to the organisation - and therefore become targets for action - across perhaps thousands of potential projects competing for a limited resource base.
Key to addressing this challenge is a method of comparing vastly diverging investment requests - weighing a diverse set of risks, benefits and costs - in order to identify optimal funding levels to balance short and long-term needs.
Given the strategic alignment of goals and values across an organisation, at least one metric must be assigned to each of the selected values in order to enable effective comparison on a quantifiable basis.
As Boudewijn Neijens, Chief Marketing Officer at Copperleaf Technologies, explains: “The value function essentially allows me to compute the contribution of the different projects which can be quite dissimilar to your overall value creation.”
However, having derived a ‘value function’ that can be used for comparison, a key requirement for asset managers is the development of a method that can be used to assess the effectiveness of various investment decisions.
So-called Asset Investment Planning and Management (AIPM) is a best practice that helps asset-intensive organisations such as utility companies make complex investment decisions with confidence.
Says Neijens: “This is all about transparent and consistent decision making.” He adds: “The thing about a value function framework is that there’s real knowledge of the criteria that you used within that model.
“It’s all a big change management process and it has taken the organisation on a journey to understand why decisions are made and how they are made.”
Inevitably, it takes time to implement an asset management system within an energy, gas or water company. The same is true of a value framework, but decision analytics can help facilitate and verify this process.
Analysing a project portfolio against a value function
At the heart of the decision analytics process is an assessment of a sample portfolio, a spread of perhaps 20 or 30 different - although typical - projects that any particular organisation may undertake over the course of a year.
“You actually start squaring them based on those metrics that you just developed and you start looking at how they score and whether they make sense,” says Neijens.
This is an iterative function and in the process of checking every one of these potential projects or investment decisions through a value function, unexpected outcomes may emerge in which projects may or may not deliver the anticipated score in the derived rankings.
As Neijens explains: “In many organisations, surprises already popped up at that level where the perception of what a project might contribute is different from the actual computed value of that project.”
Therefore some projects that might be considered very valuable may not actually score highly. Conversely, projects that are considered to be a waste of time may actually deliver significantly more value than had been anticipated.
Asset decision making timelines
This decision-analysis process should also play out over three different timelines.
In the long term, existing assets will undoubtedly degrade over time, requiring investment to maintain functionality, safety and so on.
In the shorter term, perhaps a two-year timeframe, is the need to build a budget that will include all the projects that have the highest value for the organisation.
Finally, once projects are in the execution phase, they must be monitored.
As Neijens observes: “If things are going sideways, I need to have a process that allows me to actually correct the course in such a way that I preserve as much value as possible for the organisation.”
There is, of course, an interaction between the three timelines by using predictive analytics to build a rigorous picture of long-term needs, which in turn will inform future budgets and future resource requirements.
Concludes Neijens: “At any point in time I have candidate investments on the table. They could come from the asset base and which require sustainment, or it could be new projects - green field or expansions or functional changes.
“I should be able to assess the value of every single one of those investments on a common scale. This puts me in a position to then optimise my portfolio.”